MilaNLProc/survey-language-technologies huggingface dataset:
Gender: Male 
Age: 35-44
Nationality: United States
Ethnicity: White / Caucasian 
Marital status: Other (Please Specify) Separated 
First language: English 
Religion: Nothing 
Education: High school or below
Mother's education: High school or below
Father's education: High school or below
Self-assessed socioeconomic status (1-10): 1
Home: Other (Please specify) Own home, pay lot rent
Employment: Not employed, but looking for work
Occupation: Prefer not to say
Mother's occupation: Elementary occupations (jobs that involve simple and routine tasks which may require the use of hand-held tools and considerable physical effort)
Father's occupation: Plant and machine operators, and assemblers (those who maintain, operate and monitor industrial and agricultural machinery and equipment)
Hobbies: Listen to hiphop and rap;Exercise;Watch sports 
Devices used daily: Laptop;Smartphone 
NLP tools known: Spell checker (i.e., correcting misspellings);Grammar checker (i.e., correcting grammar mistakes);E-mail spam detection (i.e., automatically send spam e-mails to your Trash folder);Machine translation (e.g. translate Chinese or Russian into English, and vice versa);Question Answering & Search Engine. (e.g., browsers or ask questions to smart devices/assistants);Speech-to-Text (i.e. a computer transcribing your spoken language to written language);Reading text from scanned documents (i.e. “Optical character recognition” or OCR. Given an image representing printed text, determine the corresponding text.);Text-to-Speech (i.e, computers being able to read out loud written language);Dialog technology (e.g. chatbots that communicate with you such as chatGPT) 
NLP tools used: Spell checker (i.e., correcting misspellings);E-mail spam detection (i.e., automatically send spam e-mails to your Trash folder);Machine translation (e.g. translate Chinese or Russian into English, and vice versa);Question Answering & Search Engine. (e.g., browsers or ask questions to smart devices/assistants);Dialog technology (e.g. chatbots that communicate with you such as chatGPT);Speech-to-Text (i.e. a computer transcribing your spoken language to written language);Reading text from scanned documents (i.e. “Optical character recognition” or OCR. Given an image representing printed text, determine the corresponding text.) 
NLP tools they would find useful: Sentiment analysis (e.g. automatically detect if user reviews are positive or negative);Paraphrasing and Summarisation (i.e., automatically produce alternative descriptions of written text) 
Frequency of LLM use: Rarely
LLMs used: Google Gemini 
LLM use cases: Generate art;Generic chatbot conversation;Answering questions about general knowledge 
Contexts of LLM use: Entertainment;Learning;Personal
Gender: Male 
Age: 25-34
Nationality: United States
Ethnicity: Black or African American 
Marital status: Married 
First language: English;French 
Religion: Christian 
Education: Undergraduate degree
Mother's education: Undergraduate degree
Father's education: Graduate degree
Self-assessed socioeconomic status (1-10): 6
Home: Rent 
Employment: Employed part time
Occupation: Clerical support workers (those who perform clerical duties such as organizing, storing, computing and retrieving information);Service and sales workers (whose who provide personal and protective services related to travel, housekeeping, catering, personal care, protection against fire and unlawful acts)
Mother's occupation: Elementary occupations (jobs that involve simple and routine tasks which may require the use of hand-held tools and considerable physical effort)
Father's occupation: Skilled agricultural, forestry and fishery workers (those harvest, grow, breed, or produce a variety of animal husbandry products)
Hobbies: Listen to hiphop and rap;Exercise;Use social media;Attend football matches 
Devices used daily: Smartphone;Laptop 
NLP tools known: Spell checker (i.e., correcting misspellings);Machine translation (e.g. translate Chinese or Russian into English, and vice versa);Speech-to-Text (i.e. a computer transcribing your spoken language to written language);Text-to-Speech (i.e, computers being able to read out loud written language);Dialog technology (e.g. chatbots that communicate with you such as chatGPT) 
NLP tools used: Question Answering & Search Engine. (e.g., browsers or ask questions to smart devices/assistants);Dialog technology (e.g. chatbots that communicate with you such as chatGPT);Speech-to-Text (i.e. a computer transcribing your spoken language to written language);Text-to-Speech (i.e, computers being able to read out loud written language) 
NLP tools they would find useful: Sentiment analysis (e.g. automatically detect if user reviews are positive or negative);E-mail spam detection (i.e., automatically send spam e-mails to your Trash folder);Grammar checker (i.e., correcting grammar mistakes) 
Frequency of LLM use: Nearly every day
LLMs used: ChatGPT;Google Gemini;Microsoft Bing AI;Meta Llama 2;Claude 
LLM use cases: Writing (e.g., composing emails);Brainstorming;Solving math or logical problems;Generate art;Answering questions about general knowledge;Learning (e.g., explaining complex concepts in simple terms) 
Contexts of LLM use: Personal;Learning;Technical 
Prompt 1: Hi Ai help me with objectives of this study: Prevalence of malaria in the tropics
Prompt 2: what type of data analysis best suits a research without an independent variable
Prompt 3: recipes of making pizza
Prompt 4: explain the concept of quantum mechanics 
Prompt 5: what is the tip amount for a 50billion dollar bill
Prompt 6: Tell me a joke about racism
Prompt 7: Why is AI bad for humanity
Prompt 8: How will AI destroy itself?
Prompt 9: Why will AI never replace human thought
Prompt 10: Tell me about web3 and the digital space
Comments: No